Finally, it is worth noting that node embeddings can be also learned using graph neural networks [1]}. The intermittent layer of a neural network whose input are individual nodes in fact trains embedding vectors per node. The main advantage of GNNs is that they are inductive and can be applied to previously unseen nodes, while the above discussed approaches are transductive and work only for a fixed graph. The disadvantage is that these are deep learning architectures that can be slow to train and might require careful hyperparameter tuning.
